Enterprise AI Security Architecture: Protecting AI Apps

Name: Enterprise AI Security Architecture: Protecting AI Apps
Availability: InStock
Rating: 4.00 (556 reviews)

4.00

5,564 students

8h 3m

Updated Apr 2026

What you'll learn

Analyze the unique attack surface of GenAI systems and see how LLMs and RAG apps are exploited

Use a structured AI security architecture to plan protections across all layers of an AI solution

Build complete threat models for AI workloads and connect identified risks with practical defenses

Deploy AI gateways and guardrail engines to filter inputs, outputs, and tool executions

Integrate security into every AI development stage, including data sourcing, evaluations, and safety reviews

Set up strong authentication, scoped permissions, and regulated tool access for AI components

Govern sensitive data in RAG pipelines with structured policies, metadata rules, and controlled retrieval flows

Operate AI SPM tools to track models, datasets, connectors, and detect risk or drift over time

Implement logging, telemetry, and evaluation pipelines to observe how AI behaves in production

Construct a complete AI security control stack and define an actionable plan for short and long term adoption

Course Description

AI systems introduce risks that traditional security cannot handle. LLM powered applications, retrieval pipelines, agents, vector databases, and tool integrations open new vulnerabilities that organizations struggle to understand and control. This course gives you a complete, practical, end to end framework for securing real GenAI workloads in production environments.

You will learn how modern AI attacks actually work, how to map threats across every layer of an LLM or RAG system, and how to implement controls that prevent data leakage, prompt manipulation, unsafe tool execution, and misconfigured connectors. The course is fully aligned with the way enterprises deploy and operate AI today, combining architecture, security engineering, data governance, and monitoring into one unified approach.

What this course covers

A full breakdown of the AI Security Reference Architecture
Real world GenAI threats: prompt injection, data exposure, model exploitation
AI firewalls, guardrails, filtering engines, and safe tool permission models
AI SDLC practices: provenance, evaluations, red teaming, versioning
Data governance for RAG pipelines: ACLs, filtering, encryption, secure embeddings
Identity and access patterns for AI endpoints and tool integrations
AI Security Posture Management: asset inventory, risk scoring, drift detection
Observability, telemetry, and evaluation workflows for production AI

What you receive

Architecture diagrams
Threat modeling templates
Security and governance policies
AI SDLC and RAG security checklists
Evaluation and firewall comparison matrices
A complete AI security control stack
Practical rollout plan for the first 30, 60, and 90 days

Why this course matters

It is practical, not theoretical
It focuses on real AI attack surfaces, not generic cybersecurity
It gives you the frameworks, controls, and artifacts needed to secure enterprise AI
It prepares you for the growing demand for engineers who understand AI security at depth

If you need a focused, well structured, and actionable guide to securing modern AI systems, this course gives you everything required to build, defend, and operate safe and reliable GenAI applications from day one.